Abstract:

Present embodiments are directed to a system and method capable of
detecting and graphically indicating physiologic patterns in patient
data. For example, present embodiments may include a monitoring system
that includes a monitor capable of receiving input relating to patient
physiological parameters and storing historical data related to the
parameters. Additionally, the monitoring system may include a screen
capable of displaying the historical data corresponding to the patient
physiological parameters. Further, the monitoring system may include a
pattern detection feature capable of analyzing the historical data to
detect a physiologic pattern in a segment of the historical data and
capable of initiating a graphical indication of the segment on the screen
when the physiologic pattern is present in the segment.

Claims:

1. A monitoring system, comprising:a monitor capable of receiving input
relating to patient physiological parameters and storing data related to
the parameters, the monitor comprising;a pattern detection feature
capable of analyzing the data to detect a pattern in the data; anda
graphical indicator capable of being displayed comprising a graphical
representation based at least in part on one or more of an occurrence,
frequency, or magnitude of the pattern.

2. The system of claim 1, wherein data related to the parameters comprises
pulse oximtery data,

3. The system of claim 1, wherein the pattern comprises an oxygen
desaturation pattern indicative of ventilatory instability.

4. The system of claim 1, wherein the graphical indicator comprises an
indicator that changes in relation to an occurrence, frequency, or
magnitude of the pattern.

5. The system of claim 1, wherein the graphical indicator comprises a
geometric shape and wherein the geometric shape is filled in relation to
one or more of an occurrence, frequency, or magnitude of the pattern.

6. The system of claim 1, wherein the pattern detection feature comprises
an index calculation feature capable of determining a scoring metric
associated with the pattern.

7. The system of claim 6, wherein the graphical indicator changes in
relation to the scoring metric calculated over time.

8. The system of claim 6, comprising an alarm that is triggered when the
scoring metric reaches a predetermined threshold.

9. The system of claim 8, wherein the predetermined threshold may be
selected by a user.

10. The system of claim 8, wherein the predetermined threshold may be
selected from a high tolerance, medium tolerance, and low tolerance
threshold.

11. The system of claim 1, wherein the graphical indicator comprises a
graphic triangle capable of filling from the bottom of the triangle to
the top of the triangle as an SPDi value increases.

12. A method, comprising:receiving input relating to patient physiological
parameters and storing data related to the parameters; anddetecting an
oxygen desaturation pattern indicative of ventilatory instability in the
data,

13. The method of claim 12, comprising displaying a graphical indicator
comprising a graphical representation based at least in part on one or
more of an occurrence, frequency, or magnitude of the oxygen desaturation
pattern indicative of ventilatory instability.

14. The method of claim 13, comprising filling the graphical indicator
based at least in part on one or more of the occurrence, frequency, or
magnitude of the oxygen desaturation pattern indicative of ventilatory
instability.

16. The method of claim 15, comprising triggering an alarm when the
scoring metric reaches a predetermined threshold.

17. The method of claim 16, comprising receiving input to set the
predetermined threshold,

18. The method of claim 16, wherein the predetermined threshold comprises
a high tolerance threshold, a medium tolerance threshold, or a low
tolerance threshold.

19. A system, comprising:a sensor capable of sensing patient physiological
parameters;a monitor capable of receiving input from the sensor related
to the patient physiological parameters and storing the data related to
the parameters, the monitor comprising;a pattern detection feature
capable of analyzing the data to detect an pattern in the data; anda
graphical indicator capable of being displayed comprising a graphical
representation based at least in part on one or more of an occurrence,
frequency, or magnitude of the pattern.

20. The system of claim 19, wherein the sensor comprises a pulse oximetry
sensor.

21. The system of claim 19, wherein the monitor comprises an alarm capable
of being triggered when the graphical indicator is empty or full.

[0003]This section is intended to introduce the reader to various aspects
of art that may be related to various aspects of the present disclosure,
which are described and/or claimed below. This discussion is believed to
be helpful in providing the reader with background information to
facilitate a better understanding of the various aspects of the present
disclosure. Accordingly, it should be understood that these statements
are to be read in this light, and not as admissions of prior art.

[0004]Patient monitors include medical devices that facilitate measurement
and observation of patient physiological data. For example, pulse
oximeters are a type of patient monitor. A typical patient monitor
cooperates with a sensor to detect and display a patient's vital signs
(e.g., temperature, pulse rate, or respiratory rate) and/or other
physiological measurements (e.g., water content of tissue, or blood
oxygen level) for observation by a user (e.g., clinician). For example,
pulse oximeters are generally utilized with related sensors to detect and
monitor a patient's functional oxygen saturation of arterial hemoglobin
(i.e., SpO2) and pulse rate. Other types of patient monitors may be
utilized to detect and monitor other physiological parameters. The use of
patient monitors may improve patient care by facilitating supervision of
a patient without continuous attendance by a human observer (e.g., a
nurse or physician).

[0005]A patient monitor may include a screen that displays information
relating to operation and use of the patient monitor. A typical patient
monitor screen may display patient data for further interpretation by a
user. For example, a pulse oximetry monitor may display data in the form
of a plethysmographic waveform or in the form of a numeric index, such as
an oxygen saturation value. However, while a monitor may convey
information to a user about the patient's condition, such information may
be difficult to interpret quickly.

BRIEF DESCRIPTION OF THE DRAWINGS

[0006]Advantages of present embodiments may become apparent upon reading
the following detailed description and upon reference to the drawings in
which:

[0007]FIG. 1 is a perspective view of an exemplary patient monitor;

[0008]FIG. 2 is a perspective view of the exemplary patient monitor in a
system with separate devices;

[0009]FIG. 3 is a representation of an exemplary display including a trend
of physiological data that exhibits a detected pattern;

[0011]FIG. 5 is an exemplary graph of SpO2 trend data with an upper
band and lower band based on mean and standard deviation values;

[0012]FIG. 6 is an exemplary graph including an SpO2 trend that
contains a ventilatory instability SpO2 pattern and a trend of the
resulting saturation pattern detection index;

[0013]FIG. 7 is an exemplary display including a graphical indicator
related to ventilatory instability;

[0014]FIG. 8 is an exemplary display including a graphical indicator
related to ventilatory instability;

[0015]FIG. 9 is an exemplary display including a graphical indicator
related to ventilatory instability;

[0016]FIG. 10 is an exemplary display including a graphical indicator
related to ventilatory instability;

[0017]FIG. 11 is an exemplary display including a graphical indicator
related to ventilatory instability;

[0018]FIG. 12 is an exemplary display including a graphical indicator
related to ventilatory instability;

[0019]FIG. 13 is an exemplary display including a graphical indicator
related to ventilatory instability;

[0020]FIG. 14 is an exemplary display including a graphical indicator
related to ventilatory instability;

[0021]FIG. 15 is an exemplary display including a graphical indicator
related to ventilatory instability;

[0022]FIG. 16 is an exemplary display including a graphical indicator
related to ventilatory instability;

[0023]FIG. 17 is an exemplary display of a menu related to alarm
management and settings for alarms related to ventilatory instability;

[0024]FIG. 18 is an exemplary display of a menu related to alarm
management and settings for alarms related to ventilatory instability;

[0025]FIG. 19 is an exemplary display of a menu related to alarm
management and settings for alarms related to ventilatory instability;
and

[0026]FIG. 20 is an exemplary flow chart of a process for alarm management
for alarms related to ventilatory instability.

DETAILED DESCRIPTION

[0027]One or more specific embodiments of the present disclosure will be
described below. In an effort to provide a concise description of these
embodiments, not all features of an actual implementation are described
in the specification. It should be appreciated that in the development of
any such actual implementation, as in any engineering or design project,
numerous implementation-specific decisions must be made to achieve the
developers' specific goals, such as compliance with system-related and
business-related constraints, which may vary from one implementation to
another. Moreover, it should be appreciated that such a development
effort might be complex and time consuming, but would nevertheless be a
routine undertaking of design, fabrication, and manufacture for those of
ordinary skill having the benefit of this disclosure.

[0028]Present embodiments may facilitate observation of certain events
(e.g., SpO2 patterns) displayed on a monitor's user-interface by
providing graphic indicators that relate to the status of certain
features. Further, present embodiments may include one or more graphic
features that are actively representative of a status of pattern
detection or a level (e.g., a percentage of an alarm level) of a detected
occurrence. Such graphic features may provide an active representation of
a gradual build up of indicators that correspond to identification of a
particular pattern or that are indicative of a severity level of an
identified condition. Indeed, present embodiments may utilize an
accumulation of data indicators to identify a physiologic pattern or a
severity level of a particular event, and the graphic feature may
gradually change as observed indications accumulate. For example, in
accordance with embodiments, ventilatory instability may be detected when
a number of certain data features have been detected within a time
period. Thus, a percentage value associated with ventilatory instability
detection may be identified by dividing the number of detected data
features by the number utilized for identification of a ventilatory
instability pattern, and the percentage may be represented in a dynamic
graphic (e.g., a status bar). As a specific example, a graphic displayed
as a triangle outline may gradually fill in the triangle outline from the
bottom as certain indicators of a particular pattern accumulate. Thus,
the triangle graphic may be completely filled in when the pattern is
actually confirmed. Likewise, the triangle may empty when certain aspects
are reduced. Similarly, a graphic may gradually fill or empty as certain
severity thresholds or indexes of a particular event are reached.

[0029]FIG. 1 is a perspective view of a patient monitor 10 in accordance
with an exemplary embodiment of the present disclosure. Specifically, the
patient monitor 10 illustrated by FIG. 1 is a pulse oximeter that is
configured to detect and monitor blood oxygen saturation levels, pulse
rate, and so forth. It should be noted that while the illustrated
embodiment includes a pulse oximeter, other embodiments may include
different types of patient monitors 10. For example, the patient monitor
10 may be representative of a vital signs monitor, a critical care
monitor, an obstetrical care monitor, or the like.

[0030]The illustrated patient monitor 10 includes a front panel 12 coupled
to a body 14 of the monitor 10. The front panel 12 includes a display
screen 16 and various indicators 18 (e.g., indicator lights and display
screen graphics) that facilitate operation of the monitor 10 and
observation of a patient's physiological metrics (e.g., pulse rate). Some
of the indicators 18 are specifically provided to facilitate monitoring
of a patient's physiological parameters. For example, the indicators 18
may include representations of the most recently measured values for
SpO2, pulse rate, index values, and pulse amplitude. In embodiments,
the indicators 18 may include an indicator related to ventilatory
instability. In an embodiment, the indicator 18 may be a triangular
indicator that is related to an index of ventilatory instability
determined by the monitor 10. When the index increases, the triangle
fills from bottom to top. In an embodiment, the indicator 18 may be a Sat
Seconds indicator that provides an indication related to low oxygen
saturation. Other indicators 18 may be specifically provided to
facilitate operation of the monitor 10, For example, the indicators 18
may include an A/C power indicator, a low battery indicator, an alarm
silence indicator, a mode indicator, and so forth. The front panel 12 may
also include a speaker 20 for emitting audible indications (e.g.,
alarms), a sensor port 22 for coupling with a sensor 24 (e.g., a
temperature sensor, a pulse oximeter sensor) and other monitor features.

[0031]Additionally, the front panel 12 may include various activation
mechanisms 26 (e.g., buttons and switches) to facilitate management and
operation of the monitor 10. For example, the front panel 12 may include
function keys (e.g., keys with varying functions), a power switch,
adjustment buttons, an alarm silence button, and so forth. It should be
noted that in other embodiments, the indicators 18 and activation
mechanisms 26 may be arranged on different parts of the monitor 10. In
other words, the indicators 18 and activation mechanisms 26 need not be
located on the front panel 12. Indeed, in some embodiments, activation
mechanisms 26 are virtual representations in a display or actual
components disposed on separate devices.

[0032]In some embodiments, as illustrated in FIG. 2, the monitor 10 may
cooperate with separate devices, such as a separate screen 28, a wireless
remote 30, and/or a keyboard 32. These separate devices may include some
of the indicators 18 and activation mechanisms 26 described above. For
example, buttons 34 on the remote 30 and/or keyboard 32 may operate as
activation mechanisms 26. Specifically, for example, the buttons 34 may
cause the monitor 10 to perform specific operations (e.g., power up,
adjust a setting, silence an alarm) when actuated on the separate device.
Similarly, the indicators 18 and/or activation mechanisms 26 may not be
directly disposed on the monitor 10. For example, the indicators 18 may
include icons, indicator lights, or graphics on the separate screen 28
(e.g., a computer screen). Further, the activation mechanisms 26 may
include programs or graphic features that can be selected and operated
via a display. It should be noted that the separate screen 28 and/or the
keyboard 32 may communicate directly or wirelessly with the monitor 10.

[0033]FIG. 3 is a representation of a display 180 that includes a trend
182 of oxygen saturation over time. As illustrated in FIG. 3, the monitor
10 may detect a cluster or pattern 184 of desaturation data, which the
monitor 10 may determine is likely indicative of sleep apnea or some
other issue. The monitor 10 may then label the pattern 184 with a textual
graphic 186 and a timestamp 188 indicating a beginning and end of the
detected pattern 184. Further, the monitor 10 may highlight or flash the
pattern, as indicated by block 190, or utilize some other graphical
indicator. In addition, the monitor may display an indicator that may
provide information to a clinician that provides information that may be
related to a patient condition. For example, the clinician may use
present embodiments to simply snap or jump to a display including the
pattern 184 (e.g., indication of sleep apnea or ventilation instability)
by activating the display control feature (e.g., pressing a button), and
the graphic indicators may draw the users attention to facilitate
diagnosis.

[0034]In order to graphically or textually indicate the patterns in
SpO2 trend data (e.g., saturation patterns indicative of ventilatory
instability), as discussed above, the patterns must first be detected.
Accordingly, present embodiments may include code stored on a tangible,
computer-readable medium (e.g., a memory) and/or hardware capable of
detecting the presence of a saturation pattern in a series of physiologic
data. For example, FIG. 4 is a block diagram of an electronic device or
pattern detection feature in accordance with present embodiments. The
electronic device is generally indicated by the reference number 200. The
electronic device 200 (e.g., an SpO2 monitor and/or memory device)
may comprise various subsystems represented as functional blocks in FIG.
4. Those of ordinary skill in the art will appreciate that the various
functional blocks shown in FIG. 4 may comprise hardware elements (e.g.,
circuitry), software elements (e.g., computer code stored on a hard
drive) or a combination of both hardware and software elements. For
example, each functional block may represent software code and/or
hardware components that are configured to perform portions of an
algorithm. Specifically, in the illustrated embodiment, the electronic
device 200 includes a reciprocation detection (RD) feature 202, a
reciprocation qualification (RQ) feature 204, a cluster determination
(CD) feature 206, a saturation pattern detection index (SPDi) calculation
feature 208, and a user notification (UN) feature 210. Each of these
components and the coordination of their functions will be discussed in
further detail below.

[0036]The RD feature 202 may be capable of performing an algorithm for
detecting reciprocations in a data trend. Specifically, the algorithm of
the RD feature 202 may perform a statistical method to find potential
reciprocation peaks and nadirs in a trend of SpO2 data. A nadir may
be defined as a minimum SpO2 value in a reciprocation. The peaks may
include a rise peak (e.g., a maximum SpO2 value in a reciprocation
that occurs after the nadir) and/or a fall peak (e.g., a maximum
SpO2 value in a reciprocation that occurs before the nadir). Once
per second, the RD feature 202 may calculate a 12 second rolling mean and
standard deviation of the SpO2 trend. Further, based on these mean
and standard deviation values, an upper band 220 and lower band 222 with
respect to an SpO2 trend 224, as illustrated by the graph 226 in
FIG. 5, may be calculated as follows:

Upper Band=mean+standard deviation;

Lower Band=mean-standard deviation.

[0037]Once the upper band 220 and lower band 222 have been determined,
potential reciprocation peaks and nadirs may be extracted from the
SpO2 trend 224 using the upper band 220 and the lower band 224.
Indeed, a potential peak may be identified as the highest SpO2 point
in a trend segment which is entirely above the upper band 220. Similarly,
a potential nadir may be identified as the lowest SpO2 point in a
trend segment that is entirely below the lower band 222. In other words,
peaks identified by the RD feature 202 may be at least one standard
deviation above the rolling mean, and nadirs identified by the RD feature
202 may be at least one standard deviation below the mean. If there is
more than one minimum value below the lower band 222, the last (or most
recent) trend point may be identified as a nadir. If more than one
maximum value is above the upper band 220, the point identified as a peak
may depend on where it is in relation to the nadir. For example,
regarding potential peaks that occur prior to a nadir (e.g., fall peaks)
the most recent maximum trend point may be used. In contrast, for peaks
that occur subsequent to a nadir (e.g., rise peaks), the first maximum
point may be used. In the example trend data represented in FIG. 5, a
peak and nadir is detected approximately every 30-60 seconds.

[0038]In one embodiment, a window size for calculating the mean and
standard deviation may be set based on historical values (e.g., average
duration of a set number of previous reciprocations). For example, in one
embodiment, a window size for calculating the mean and standard deviation
may be set to the average duration of all qualified reciprocations in the
last 6 minutes divided by 2. In another embodiment, an adaptive window
method may be utilized wherein the window size may be initially set to 12
seconds and then increased as the length of qualified reciprocations
increases. This may be done in anticipation of larger reciprocations
because reciprocations that occur next to each other tend to be of
similar shape and size. If the window remained at 12 seconds, it could
potentially be too short for larger reciprocations and may prematurely
detect peaks and nadirs. The following equation or calculation is
representative of a window size determination, wherein the output of the
filter is inclusively limited to 12-36 seconds, and the equation is
executed each time a new reciprocation is qualified:

[0039]If no qualified reciprocations in the last 6 minutes:

Window Size=12 (initial value)

else:

RecipDur=1/2*current qualified recip duration+1/2*previous RecipDur

Window Size=bound(RecipDur,12,36).

[0040]With regard to SpO2 signals that are essentially flat, the
dynamic window method may fail to find the three points (i.e., a fall
peak, a rise peak, and a nadir) utilized to identify a potential
reciprocation. Therefore, the RD feature 202 may limit the amount of time
that the dynamic window method can search for a potential reciprocation.
For example, if no reciprocations are found in 240 seconds plus the
current adaptive window size, the algorithm of the RD feature 202 may
timeout and begin to look for potential reciprocations at the current
SpO2 trend point and later. The net effect of this may be that the
RD feature 202 detects potential reciprocations less than 240 seconds
long.

[0041]Once potential peaks and nadirs are found using the RD feature 202,
the RQ feature 204 may pass the potential reciprocations through one or
more qualification stages to determine if a related event is caused by
ventilatory instability. A first qualification stage may include checking
reciprocation metrics against a set of limits (e.g., predetermined hard
limits). A second qualification stage may include a linear qualification
function. In accordance with present embodiments, a reciprocation may be
required to pass through both stages in order to be qualified.

[0042]As an example, in a first qualification stage, which may include a
limit-based qualification, four metrics may be calculated for each
potential reciprocation and compared to a set of limits. Any
reciprocation with a metric that falls outside of these limits may be
disqualified. The limits may be based on empirical data. For example, in
some embodiments, the limits may be selected by calculating the metrics
for potential reciprocations from sleep lab data where ventilatory
instability is known to be present, and then comparing the results to
metrics from motion and breathe-down studies. The limits may then be
refined to filter out true positives.

[0043]The metrics referred to above may include fall slope, magnitude,
slope ratio, and path length ratio. With regard to fall slope, it may be
desirable to limit the maximum fall slope to filter out high frequency
artifact in the SpO2 trend, and limit the minimum fall slope to
ensure that slow SpO2 changes are not qualified as reciprocations.
Regarding magnitude, limits may be placed on the minimum magnitude
because of difficulties associated with deciphering the difference
between ventilatory instability reciprocations and artifact
reciprocations as the reciprocation size decreases, and on the maximum
magnitude to avoid false positives associated with sever artifact (e.g.,
brief changes of more than 35% SpO2 that are unrelated to actual
ventilatory instability). The slope ratio may be limited to indirectly
limit the rise slope for the same reasons as the fall slope is limited
and because ventilatory instability patterns essentially always have a
desaturation rate that is slower than the resaturation (or recovery)
rate. The path length ratio may be defined as Path Length/((Fall
Peak-Nadir)+(Rise Peak-Nadir)), where Path Length=Σ|Current
SpO2 Value-Previous SpO2 value| for all SpO2 values in a
reciprocation, and the maximum path length ratio may be limited to limit
the maximum standard deviation of the reciprocation, which limits high
frequency artifact. The following table (Table I) lists the
above-identified metrics along with their associated equations and the
limits used in accordance with one embodiment:

[0044]As indicated in Table I above, an oximetry algorithm in accordance
with present embodiments may operate in two response modes: Normal
Response Mode or Fast Response Mode. The selected setting may change the
SpO2 filtering performed by the oximetry algorithm, which in turn
can cause changes in SpO2 patterns. Therefore a saturation pattern
detection feature may also accept a response mode so that it can account
for the different SpO2 filtering. Table I indicates values
associated with both types of response mode with regard to the Fall Slope
values.

[0045]A second qualification stage of the RQ feature 204 may utilize a
object reciprocation qualification feature. Specifically, the second
qualification stage may utilize a linear qualification function based on
ease of implementation, efficiency, and ease of optimization. The
equation may be determined by performing a least squares analysis. For
example, such an analysis may be performed with MATLAB®. The inputs
to the equation may include the set of metrics described below. The
output may be optimized to a maximum value for patterns where ventilatory
instability is known to be present. The equation may be optimized to
output smaller values (e.g., 0) for other data sets where potential false
positive reciprocations are abundant.

[0046]To simplify optimization, the equation may be factored into
manageable sub-equations. For example, the equation may be factored into
sub-equation 1, sub-equation D, and sub-equation 2, as will be discussed
below. The output of each sub-equation may then be substituted into the
qualification function to generate an output. The outputs from each of
the sub-equations may not be utilized to determine whether a
reciprocation is qualified in accordance with present embodiments.
Rather, an output from a full qualification function may be utilized to
qualify a reciprocation. It should be noted that the equations set forth
in the following paragraphs describe one set of constants. However,
separate sets of constants may be used based on the selected response
mode. For example, a first set of constants may be used for the Normal
Response Mode and a second set of constants may be used for the Fast
Response Mode.

[0047]Preprocessing may be utilized in accordance with present embodiments
to prevent overflow for each part of the qualification function. The
tables (Tables II-VII) discussed below, which relate to specific
components of the qualification function may demonstrate this overflow
prevention. Each row in a table contains the maximum value of term which
is equal to the maximum value of the input variable multiplied by the
constant, wherein the term "maximum" may refer to the largest possible
absolute value of a given input. Each row in a table contains the maximum
intermediate sum of the current term and all previous terms. For example,
a second row may contain the maximum output for the second term
calculated, as well as the maximum sum of terms 1 and 2. It should be
noted that the order of the row may match the order that the terms are
calculated by the RQ feature 204. Further, it should be noted that in the
tables for each sub-equation below, equations may be calculated using
temporary signed 32-bit integers, and, thus, for each row in a table
where the current term or intermediate term sum exceeds 2147483647 or is
less than -2147483647 then an overflow/underflow condition may occur.

[0048]A first sub-equation, sub-equation 1, may use metrics from a single
reciprocation. For example, sub-equation 1 may be represented as follows:

where SrCf, PdCf, FsCf, PrCf, and Eq1Offset may be selected using least
squares analysis (e.g., using MATLAB®). PeakDiff may be defined as
equal to |Recip Fall Peak-Recip Rise Peak|. It should be noted that
PeakDiff is typically not considered in isolation but in combination with
other metrics to facilitate separation. For example, a true positive
reciprocation which meets other criteria but has a high peak difference
could be an incomplete recovery. That is, a patient's SpO2 may drop
from a baseline to a certain nadir value, but then fail to subsequently
recover to the baseline. However, when used in combination with other
metrics in the equation, PeakDiff may facilitate separation of two
classifications, as large peak differences are more abundant in false
positive data sets.

[0049]With regard to sub-equation 1, the tables (Tables II and III) set
forth below demonstrate that the inputs may be preprocessed to prevent
overflow. Further, the tables set forth below include exemplary limits
that may be utilized in sub-equation 1 in accordance with present
embodiments. It should be noted that Table II includes Fast Response Mode
constants and Table III includes Normal Response Mode constants.

[0050]A second sub-equation, sub-equation D, may correspond to a
difference between two consecutive reciprocations which have passed the
hard limit qualifications checks, wherein consecutive reciprocations
include two reciprocations that are separated by less than a defined time
span. For example, consecutive reciprocations may be defined as two
reciprocations that are less than 120 seconds apart. The concept behind
sub-equation D may be that ventilatory instability tends to be a
relatively consistent event, with little change from one reciprocation to
the next. Artifact generally has a different signature and tends to be
more random with greater variation among reciprocations. For example, the
following equation may represent sub-equation D:

where, SrDCf, DDCf, NdCf, PrDCf, and EqDOffset may be selected using least
squares analysis (e.g., using MATLAB®). With regard to other
variables in sub-equation D, SlopeRatioDiff may be defined as |Current
Recip Slope Ratio-Slope Ratio of last qualified Recip|; DurationDiff may
be defined as |Current Recip Duration-Duration of last qualified Recip|;
NadirDiff may be defined as |Current Recip Nadir-Nadir value of last
qualified Recip|; and PathLengthRatioDiff may be defined as |Current
Recip Path Length Ratio-Path Length Ratio of last qualified Recip|.

[0051]With regard to sub-equation D, the tables (Tables IV and V) set
forth below demonstrate that the inputs may be preprocessed to prevent
overflow. Further, the tables set forth below include exemplary limits
that may be utilized in sub-equation D in accordance with present
embodiments. It should be noted that Table IV includes Fast Response Mode
constants and Table V includes Normal Response Mode constants.

[0052]A third sub-equation, sub-equation 2, may combine the output of
sub-equation D with the output of sub-equation 1 for a reciprocation
(e.g., a current reciprocation) and a previous reciprocation. For
example, the following equation may represent sub-equation 2:

where DCf, N1Cf, PrevEq1Cf, and Eq2Offset may be selected using least
squares analysis (e.g., using MATLAB®). With regard to other
variables in sub-equation 2, EqDScore may be described as the output of
sub-equation D; Eq1ScoreCurrent may be described as the output of
sub-equation 1 for a current reciprocation; and Eq1ScorePrev may be
described as the output of sub-equation 1 for the reciprocation previous
to the current reciprocation.

[0053]With regard to sub-equation 2, the tables (Tables VI and VII) set
forth below demonstrate that the inputs may be preprocessed to prevent
overflow. Further, the tables set forth below include exemplary limits
that may be utilized in sub-equation 2 in accordance with present
embodiments. It should be noted that Table VI includes Fast Response Mode
constants and Table VII includes Normal Response Mode constants.

[0054]A qualification function may utilize the output of each of the
equations discussed above (i.e., sub-equation 1, sub-equation D, and
sub-equation 2) to facilitate qualification and/or rejection of a
potential reciprocation. For example, the output of the qualification
function may be filtered with an IIR filter, and the filtered output of
the qualification function may be used to qualify or reject a
reciprocation. An equation for an unfiltered qualification function
output in accordance with present embodiments is set forth below:

where Eq2Cf, ConsecCf, MaxCf, ArtCf, and QFOffset may be selected using
least squares analysis (e.g., using MATLAB®), and, as indicated
above, Eq1Score may be defined as the output of sub-equation 1.

[0055]Other metrics in the unfiltered qualification function include
SingleRecipWt, MultipleRecipWt, NConsecRecip, RecipMax, and Artifact %.
With regard to SingleRecipWt and MultipleRecipWt, when there are two or
more consecutive qualified reciprocations (e.g., qualified reciprocations
that are less than 120 seconds apart) present, SingleRecipWt may equal 0
and MultipleRecipWt may equal 1. However, when only a single
reciprocation is present, SingleRecipWt may equal 1 and MultipleRecipWt
may equal 0.

[0056]NConseRecip, which may be defined as equal to
max(NConsecRecip',QFConsecMax), may include a count of the number of
consecutive reciprocations (e.g., reciprocations that are less than or
equal to 120 seconds apart) that have passed the hard limit checks. The
value for NConsecRecip may be reset to 0 whenever a gap between any two
partially qualified reciprocations exceeds 120 seconds. This may be based
on the fact that ventilatory instability is a relatively long lasting
event as compared to artifact. Therefore, as more reciprocations pass the
hard limit checks, the qualification function may begin qualifying
reciprocations that were previously considered marginal. However, to
guard against a situation where something is causing a longer term
artifact event (e.g., interference from nearby equipment), the value may
be clipped to a maximum value to limit the metrics influence on the
qualification function output.

[0057]RecipMax, which may be defined as equal to max(Fall Peak, Rise
Peak), may facilitate making decisions about marginal reciprocations.
Indeed, marginal reciprocations with higher maximum SpO2 values may
be more likely to get qualified than marginal reciprocations with lower
SpO2 values. It should be noted that this metric works in tandem
with the NConsecRecip metric, and multiple marginal reciprocations with
lower maximum SpO2 values may eventually, over a long period of
time, get qualified due to the NConsecRecip metric.

[0058]The metric Artifact % may be defined as an artifact percentage that
is equal to 100*Total Artifact Count/Recip Duration, where Total Artifact
Count is the number of times and artifact flag was set during the
reciprocation. Present embodiments may include many metrics and equations
that are used to set the artifact flag. Because of this it is a generally
reliable indication of the amount of artifact present in the oximetry
system as a whole. Marginal reciprocations with a high Artifact % are
less likely to be qualified than marginal reciprocations with a low (or
0) artifact percentage.

[0059]A last component of the qualification function may include an
infinite impulse response (IIR) filter that includes coefficients that
may be tuned manually using a tool (e.g., a spreadsheet) that models
algorithm performance. The filtered qualification function may be
represented by the following equation, which includes different constants
for different modes (e.g., Fast Response Mode and Normal Response Mode):

where QFUnfiltered may be defined as the current unfiltered qualification
function output; PrevQFFiltered may be defined as the previous filtered
qualification function output; and where the constat "a" may be set to
0.34 for Fast Response Mode and 0.5 for Normal Response Mode.

[0060]The filtered output of the qualification function may be compared to
a threshold to determine if the current reciprocation is the result of
RAF or artifact. The optimum threshold may theoretically be 0.5. However,
an implemented threshold may be set slightly lower to bias the output of
the qualification function towards qualifying more reciprocations, which
may result in additional qualification of false positives. The threshold
may be lowered because, in accordance with present embodiments, a cluster
determination portion of the algorithm, such as may be performed by the
CD feature 206, may require a certain number (e.g., 5) of fully qualified
reciprocations before an index may be calculated, and a certain number
(e.g., at least 2) of consecutive qualified reciprocations (with no
intervening disqualified reciprocations) within the set of fully
qualified reciprocations. Since multiple reciprocations may be required,
the clustering detection method may be biased toward filtering out false
positives. Accordingly, the reciprocation qualification function
threshold may be lowered to balance the two processes.

[0061]The CD feature 206 may be capable of performing an algorithm that
maintains an internal reciprocation counter that keeps track of a number
of qualified reciprocations that are currently present. When the
reciprocation counter is greater than or equal to a certain value, such
as 5, the clustering state may be set to "active" and the algorithm may
begin calculating and reporting the SPDi. When clustering is not active
(e.g., reciprocation count <5) the algorithm may not calculate the
SPDi. The SPDi may be defined as a scoring metric associated with the
identification of a saturation trend pattern generated in accordance with
present embodiment and may correlate to ventilatory instability in a
population of sleep lab patients.

[0062]The CD feature 206 may utilize various rules to determine the
reciprocation count. For example, when the clustering state is inactive,
the following rules may be observed: [0063]1.) If the distance between
qualified reciprocation exceeds 120 seconds, then the reciprocation
count=0; [0064]2.) If the current reciprocation is qualified, and the
time from the start of the current reciprocation to the end of the last
qualified reciprocation is <=120 seconds, then the reciprocation
count=reciprocation count+1; [0065]3.) If the current reciprocation is
not qualified, then the reciprocation count=max(reciprocation count-2,
0).Once clustering is active, it may remain active until the time between
two qualified reciprocations exceeds 120 seconds. The following table
(Table II) illustrates an example of how the reciprocation count rules
may be applied to determine a clustering state.

[0066]When the clustering state is active, the SPDi calculation feature
208 may calculate an unfiltered SPDi for each new qualified
reciprocation. The following formula may be used by the SPDi calculation
feature 208:

Unfiltered SPDi=a*Magnitude+b*PeakDelta+c*NadirDelta; [0067]wherein
a=1.4, b=2.0, c=0.2; [0068]wherein Magnitude=average magnitude of all
reciprocations in the last 6 minutes; [0069]wherein PeakDelta average of
the three highest qualified reciprocation rise peaks in the last 6
minutes minus the average of the three lowest qualified reciprocation
rise peaks in the last 6 minutes; and [0070]wherein NadirDelta=average of
the three highest qualified reciprocation nadirs in the last 6 minutes
minus the average of the three lowest qualified reciprocation nadirs in
the last 6 minutes. [0071]Wherein SPDi <=31

[0072]The above formula may be utilized to quantify the severity of a
ventilatory instability pattern. The constants and metrics used may be
based on input from clinical team members. It should be noted that the
PeakDelta parameter may be assigned the largest weighting constant since
the most severe patterns generally have peak reciprocation values that do
not recover to the same baseline.

[0073]The unfiltered SPDi may be updated whenever clustering is active and
a new qualified reciprocation is detected. Non-zero SPDi values may be
latched for a period of time (e.g., 6 minutes). The unfiltered SPDi may
then be low pass filtered to produce the final output SPDi value. The
following IIR filter with a response time of approximately 40 seconds may
be used:

SPDi=Unfiltered SPDi/a+Previous Filtered SPDi*(a-1)/a;

[0074]wherein a=40.

[0075]FIG. 6 is an exemplary graph 260 including an SpO2 trend 262
that contains a ventilatory instability SpO2 pattern and a trend of
the resulting SPDi 264. In the illustrated example, it should be noted
that the SPDi is sensitive to the decreasing peaks (incomplete
recoveries) starting at approximately t=6000.

[0076]The UN feature 210 may be capable of determining if a user
notification function should be employed to notify a user (e.g., via a
graphical or audible indicator) of the presence of a detected patterns
such as ventilatory instability. The determination of the UN feature 210
may be based on a user configurable tolerance setting and the current
value of the SPDi. For example, the user may have four choices for the
sensitivity or tolerance setting: Off, Low, Medium, and High. When the
sensitivity or tolerance setting is set to Off, an alarm based on
detection of a saturation pattern may never be reported to the user. The
other three tolerance settings (i.e., Low, Medium, and High) may each map
to an SPDi threshold value. For example, Low may map to an SPDi threshold
of 6, Medium may map to an SPDi threshold of 15, and High may map to an
SPDi threshold of 24. The thresholds may be based on input from users.
When the SPDi is at or above the threshold for a given tolerance setting,
the user may be notified that ventilatory instability is present. As
discussed below, the indication to the user may include a graphical
designation of the trend data corresponding to the detected pattern. For
example, the trend data utilized to identify a ventilatory instability
pattern may be highlighted, flashing, or otherwise indicated on a user
interface of a monitor in accordance with present embodiments. Similarly,
parameters such as the SPDi value and the tolerance settings may be
graphically presented on a display.

[0077]In embodiments, the display may include a graphical indicator that
may provide information to a user related to the occurrence, frequency,
and/or magnitude of the patterns detected. The information may be based
on the SPDi index, which is proportional to the magnitude and variability
of qualified reciprocations. The SPD calculation feature may be capable
of notifying a user of ventilatory instability that corresponds to a
certain SPDi index value. In embodiments, when the SPDi is at or above a
threshold setting, the user may be notified via a graphical indicator
600.

[0078]As illustrated in FIG. 7, the graphical indicator 600 may be
represented on display 598 as a dashed triangle that may graphically fill
from top to bottom as a monitored and/or calculated value increases. For
example, in one embodiment, the graphical indicator 600 may gradually
fill as the SPDi index calculated by the SPDi calculation feature 208
increases. Further, the graphical indicator 600 may include a tolerance
level indicator 602 that displays an index, for example 1, 2, or 3, for
tolerance or sensitivity settings of High, Medium, and Low, respectively,
for the SPDi calculation feature 208. The tolerance settings may set the
threshold for triggering a change in the graphical indicator 600 and/or
for triggering SPD-associated alarms. As shown in FIG. 9, the graphical
indicator 600 may be empty, indicating that an SPDi index is below a
certain threshold.

[0079]In addition, the display 598 may also include additional indicators,
such as a Sat Seconds indicator 604 that relates to oxygen saturation
information. Sat Seconds indicators may assist clinicians in focusing on
desaturations related to a patient condition rather than short
desaturations that may be the result of measurement anomalies. As shown,
the Sat Seconds indicator 604 may be partially full while the graphical
indicator 600 is empty. The Sat Seconds indicator 604 may display results
determined by a Sat Second analyzing function, which in an embodiment
analyzes desaturation events by multiplying their duration (seconds) by
the number of percentage points the patient exceeds the alarm limit. In
an embodiment, the Sat Seconds analyzer may determine if an oxygen
desaturation event has occurred by analyzing a plot of oxygen saturation
versus time. The Sat Seconds analyzer may integrate the area under the
curve of time spent below a certain oxygen saturation threshold.
Accordingly, sudden, short desaturation readings that may be measurement
noise (e.g., that otherwise may trigger nuisance alarms) may be
eliminated from a Sat Seconds counter clock while more prolonged
desaturations may be counted. Clinicians can set the SatSeconds limit, or
clock, to 10, 25, 50 or 100 SatSeconds. In an embodiment, the clock may
be set to 100, and therefore only events that equal or surpass the 100
SatSeconds limit may trigger an alarm. In addition, the Sat Seconds
indicator 604 may fill up in relation to the Sat Seconds count. For
example, the indicator 604 may be full when the count reaches 100.

[0080]While the Sat Seconds indicator 604 may manage nuisance alarms
related to desaturation events, the graphical indicator 600 may display
information determined by not only the duration and magnitude of the
oxygen desaturation, but also to the patterns of the desaturation events,
as provided herein. Such analysis may provide information to the
healthcare provider about ventilatory instability that may, for example,
be related to sleep apnea. Turning to the graphical indicator 600, which
provides information to a clinician related to ventilatory instability,
FIG. 8 shows a display screen 620 in which the graphical indicator 600
has started to fill up from the bottom. The "filling up" may represent
the addition of a fill (e.g., any color pixels) to the area of the
triangle. In one embodiment, the graphical indicator 600 may fill up when
the calculated SPDi index is higher than a tolerance setting. As noted,
the High Tolerance, Medium Tolerance, and Low Tolerance alarm limits may
refer to certain default values of the SPDi index, such as 24, 15, and 6,
respectively. When the SPDi index is higher than, for example, 24 (High
Tolerance setting), the graphical indicator 600 may begin to fill. In an
embodiment, the graphical indicator 600 may begin to fill up when the
SPDi index is lower than but near 24, whereby an SPDi index of 24
represents a "full" state. In such an embodiment, the approximately 25%
full graphical indicator as shown may represent an SPDi index of, for
example, 18.

[0081]FIG. 9 is a display 640 including an indicator 600 that is
approximately 50% full. As noted, the graphical indicator 600 may
continue to fill as the SPDi index rises over time. The SPDi index may be
calculated over a rolling period of time. In embodiments, the SPDi index
may be calculated over a 240 second window. If, during this window of
time, the SPDi index increases as a result an increase in measured recip
frequency or magnitude parameters used to determine the index, the
graphical indicator 600 may continue to fill up.

[0082]FIG. 10 is an exemplary display 660 showing a graphical indicator
600 that is approximately 75% full, and FIG. 11 is an exemplary display
680 showing a graphical indicator 600 that is approximately 100% full. As
shown in FIGS. 7-11, the indicator 600 may fill up as a percentage or
fraction of the total indicator space as the SPDi index increases. For
example, the indicator 600 may have five possible display states: empty,
25% full, 50% full, 75% full, or 100% full. In embodiments, the indicator
600 may fill in any suitable manner. For example, a graphical indicator
may have any number of fill states, e.g., filling up in 10%, 20%, 25%, or
50% increments. In other embodiments, the indicator 600 may also change
in intensity to indicate increasing ventilatory instability. For example,
an indicator may fill in uniformly, but with increasing intensity, as the
SPDi index increases. In an embodiment, the indicator 600 may have states
that resemble different values on a grayscale, with the percentage
grayscale increasing at the SPDi index increases.

[0083]A filled state of the graphical indicator 600 may trigger a primary
or secondary alarm. In an embodiment, a primary alarm, such as a text
alert, may be triggered when the graphical indicator 600 begins to fill.
When the indicator 600 has reached a full state, a secondary alarm, such
as an audio alarm, may then be triggered.

[0084]The indicator 600 may be displayed on any number of monitor views to
provide information to a healthcare provider during various monitoring
activities. FIG. 12 shows an exemplary general pleth display 700 with a
plethysmographic waveform 702. The display 700 may include a graphical
indicator 600 for saturation pattern detection with a tolerance indicator
602. The display may also include softkeys 704 for navigating between
other display views.

[0085]FIG. 13 shows an exemplary blip display 720. As shown, the location
of the graphical indicator 600 on the screen may change according to the
particular display view chosen. However, the general shape of the
indicator 600 may remain substantially the same so that the user may
easily identify the indicator 600. FIG. 14 shows an exemplary general
care format view 760. As shown, the graphical indicator 600 and the Sat
Seconds indicator 604 may be relatively larger in certain views. FIG. 15
shows a real-time trend display 780 with a trend xy plot 782. FIG. 16
shows a display 786 in which an SPD event 788 is highlighted on the xy
plot 782. In FIG. 15 and FIG. 16, the graphical indicator 600 may be
displayed along with other indicators and patient data.

[0086]In embodiments, a user may have the ability to change certain
settings on the monitor 10 related to the graphical indicator 600. In one
embodiment, a user may be able to change settings related to SPD alarm
limits. An alarm setup display related to the SPD alarm settings may be
accessed via softkey from other display screens. FIG. 17 is an exemplary
alarm setup display 800. As shown, a user may be able to select an option
in which the monitor 10 activates SPD calculation features and associated
indicators and alarms. In addition, a user may activate a Sat Seconds
calculation and/or display feature. In an embodiment, a user may be able
to select between audio and/or visual alarms in response to saturation
pattern detection by the monitor 10, as shown in FIG. 18, which depicts a
display 820 in which a user may select to turn off audio alerts related
to saturation pattern detection.

[0087]In another embodiment, a user may be able to change the default
values on the limits to user-selected values. FIG. 19 is an exemplary
display 840 showing an SPD Tolerance menu. A user may select between
multiple SPD tolerance settings for High, Medium, or Low Tolerance of the
SPD-associated alarms. In an embodiment, a monitor 10 may store certain
default values associated with SPDi index values. These default values
may be determined based on clinical observations of a test patient
population or other input from healthcare providers. For example, the
default High Tolerance value may be associated with an SPDi index value
of 24. Accordingly, any SPD-associated alarms may not trigger until the
SPDi index for a calculated window of time is at or near 24.

[0088]In another embodiment, a user may input specific values for High,
Medium, and Low Tolerance limits. A user may select any value, so long as
the High Tolerance limit is higher than the Medium Tolerance limit, and
the Medium Tolerance limit is higher than the Low Tolerance limit. A
monitor 10 may be able trigger an error message if a user attempts to set
a limit of less than zero or if a user attempts to set a High Tolerance
limit that is lower than a Medium Tolerance limit, and so on.

[0089]FIG. 20 is a flow chart 900 indicating how a monitor 10 may trigger
alarms based on the SPDi tolerance settings. At start 902, if a tolerance
setting is set to "OFF" at 904, the process sets the alarm status to "NO
SPD ALARM" at 905. If the tolerance is set to Low (906), Medium (908), or
High (910), the SPDi index is compared to the appropriate threshold,
depending on the setting. For example, if the tolerance is set to Low at
906, the SPDi index is compared to the Low Index Limit at 912. If the
SPDi index is lower that the Low Index Limit, the process may set the
alarm status to "NO SPD ALARM" at 905. If the SPDi index is higher than
the Low Index Limit, the process may then determine if audio alerts have
been enabled at 914. If such alerts have not been enabled, the process
set the alarm status to "VISUAL ONLY" to trigger visual alarms at 916. If
audible alerts have been enabled, the alarm status may be set to "AUDIBLE
VISUAL" at 918 for triggering audible and visual alarms before the
process ending at 920. Similarly, a Medium Tolerance setting may be
compared to a Medium Index Limit at 922 and a High Tolerance setting may
be compared to a High Index Limit at 924.

[0090]While the embodiments of the present disclosure may be susceptible
to various modifications and alternative forms, specific embodiments have
been shown by way of example in the drawings and will be described in
detail herein. However, it should be understood that the present
embodiments are not intended to be limited to the particular forms
disclosed. Rather, present embodiments are to cover all modifications,
equivalents and alternatives falling within the spirit and scope of
present embodiments as defined by the following appended claims.